Item nonresponse occurs frequently in sample surveys and other applications. Imputation is commonly used to fill in the missing item values in a random sample {Yi;i=1,...,n}. Fractional linear regression imputation, based on the model with independent zero mean errors [epsilon]i, is used to...

In this paper, we discuss the construction of the confidence intervals for the regression vector [beta] in a linear model under negatively associated errors. It is shown that the blockwise empirical likelihood (EL) ratio statistic for [beta] is asymptotically [chi]2-type distributed. The result...

We propose likelihood and restricted likelihood ratio tests for goodness-of-fit of nonlinear regression. The first-order Taylor approximation around the MLE of the regression parameters is used to approximate the null hypothesis and the alternative is modeled nonparametrically using penalized...

In this article we study a semiparametric generalized partially linear model when the covariates are missing at random. We propose combining local linear regression with the local quasilikelihood technique and weighted estimating equation to estimate the parameters and nonparameters when the...

In this paper, we define two restricted estimators for the regression parameters in a multiple linear regression model with measurement errors when prior information for the parameters is available. We then construct two sets of improved estimators which include the preliminary test estimator,...

Let (X1, Y1), (X2, Y2), ..., be d+1 dimensional random vectors which are distributed as (X, Y). Let [theta](x) be the conditional median, that is, [theta](x)=inf{y: P(Y[less-than-or-equals, slant]y | X=x)[greater-or-equal, slanted]1/2}. We consider the problem of...

Clustered data arise commonly in practice and it is often of interest to estimate the mean response parameters as well as the association parameters. However, most research has been directed to address the mean response parameters with the association parameters relegated to a nuisance role....

We study nonlinear regression models whose both response and predictors are measured with errors and distorted as single-index models of some observable confounding variables, and propose a multicovariate-adjusted procedure. We first examine the relationship between the observed primary...

The Stein-rule (SR) and positive-part Stein-rule (PSR) estimators are two popular shrinkage techniques used in linear regression, yet very little is known about the robustness of these estimators to the disturbances' deviation from the white noise assumption. Recent studies have shown that the...

This paper derives the corrected conditional Akaike information criteria for generalized linear mixed models by analytic approximation and parametric bootstrap. The sampling variation of both fixed effects and variance component parameter estimators are accommodated in the bias correction term....